Ballistic movements like saccades require the brain to generate motor commands without the benefit of sensory feedback. Despite this, saccades are remarkably accurate. Theory suggests that this accuracy arises because the brain relies on an internal forward model that monitors the motor commands, predicts their sensory consequences, and corrects eye trajectory midflight. If control of saccades relies on a forward model, then the forward model should adapt whenever its predictions fail to match sensory feedback at the end of the movement. Using optimal feedback control theory, we predicted how this adaptation should alter saccade trajectories. We trained subjects on a paradigm in which the horizontal target jumped vertically during the saccade. With training, the final position of the saccade moved toward the second target. However, saccades became increasingly curved, i.e., suboptimal, as oculomotor commands were corrected on-line to steer the eye toward the second target. The adaptive response had two components: (1) the motor commands that initiated the saccades changed slowly, aiming the saccade closer to the jumped target. The adaptation of these earliest motor commands displayed little forgetting during the rest periods. (2) Late in saccade trajectory, another adaptive response steered it still closer to the jumped target, producing curvature. Adaptation of these late motor commands showed near-complete forgetting during the rest periods. The two components adapted at different timescales, with the late-acting component displaying much faster rates. It appears that in controlling saccades, the brain relies on an internal feedback that has the characteristics of a fast-adapting forward model.

It is possible that motor adaptation in timescales of minutes is supported by two distinct processes: one process that learns slowly from error but has strong retention, and another that learns rapidly from error but has poor retention. This two-state model makes the prediction that if a period of adaptation is followed by a period of reverse-adaptation, then in the subsequent period in which errors are clamped to zero (error-clamp trials) there will be a spontaneous recovery, i.e., a rebound of behavior toward the initial level of adaptation. Here we tested and confirmed this prediction during double-step, on-axis, saccade adaptation. When people adapted their saccadic gain to a magnitude other than one (adaptation) and then the gain was rapidly reversed back to one (reverse-adaptation), in the subsequent error-clamp trials (visual target placed on the fovea after the saccade) the gain reverted toward the initially adapted value and then gradually reverted toward normal. We estimated that the fast system was about 20-fold more sensitive to error than the slow system, but had a time constant of 28 s, whereas the slow system had a time constant of nearly 8 min. Therefore short-term adaptive mechanisms that maintain accuracy of saccades rely on a memory system that has characteristics of a multistate process with a logarithmic distribution of timescales.

In a typical short-term saccadic adaptation protocol, the target moves intrasaccadically either toward (gain-down) or away (gain-up) from initial fixation, causing the saccade to complete with an endpoint error. A central question is how the motor system adapts in response to this error: are the motor commands changed to bring the eyes to a different goal, akin to a remapping of the target, or is adaptation focused on the processes that monitor the ongoing motor commands and correct them midflight, akin to changes that act via internal feedback? Here, we found that, in the gain-down paradigm, the brain learned to produce a smaller amplitude saccade by altering the trajectory of the saccade. The adapted saccades had reduced peak velocities, reduced accelerations, shallower decelerations, and increased durations compared with a control saccade of equal amplitude. These changes were consistent with a change in an internal feedback that acted as a forward model. However, in the gain-up paradigm, the brain learned to produce a larger amplitude saccade with trajectories that were identical with those of control saccades of equal amplitude. Therefore, whereas the gain-down paradigm appeared to induce adaptation via an internal feedback that controlled saccades midflight, the gain-up paradigm induced adaptation primarily via target remapping. Our simulations explained that, for each condition, the specific adaptation produced a saccade that brought the eyes to the target with the smallest motor costs.

The saccadic system is an ideal model for the study of how the brain optimizes its motor behavior. Here we review some recent research that points to exciting new areas of investigation relative to the multiple time scales of and the influence of context and consolidation on motor learning. These findings suggest new ways of thinking about the processes that underlie the short-term adaptive mechanisms that maintain accuracy of eye movements and so ensure optimal vision.

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